Plan explicability and predictability for robot task planning

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave in unexpected ways. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in “natural ways”, and work that investigated legible motion planning, there is no general solution for high level task planning. To address this issue, we introduce the notions of plan explicability and predictability. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with a physical robot to demonstrate the effectiveness of our approach.

Authors

• Yu Zhang 0055
• Sarath Sreedharan
• Anagha Kulkarni 0002
• Tathagata Chakraborti
• Hankz Hankui Zhuo
• Subbarao Kambhampati

Keywords

• Planning
• Robots
• Labeling
• Computational modeling
• Training
• Predictive models
• Task Planning
• Learning Models
• Cognitive Load
• Path Planning
• Training Examples
• Labeling Strategy
• Labeling Process
• Conditional Random Field
• Intelligent Robots
• Physical Robot
• Sequence Of Actions
• Multiple Tasks
• Intelligence Agencies
• Test Problems
• Human Perspective
• Human-robot Interaction
• Autonomous Agents
• Storage Area
• Hidden Variables
• Public Goals
• EU Energy
• Human Labeling
• Human Interpretation
• Action Labels
• Sequence Labeling
• Labeling Task
• Conditional Random Field Model
• Level Oscillations
• Planning Cost
• Number Of Goals